//  SMPSO.java
//
//  Author:
//       Antonio J. Nebro <antonio@lcc.uma.es>
//       Juan J. Durillo <durillo@lcc.uma.es>
//
//  Copyright (c) 2011 Antonio J. Nebro, Juan J. Durillo
//
//  This program is free software: you can redistribute it and/or modify
//  it under the terms of the GNU Lesser General Public License as published by
//  the Free Software Foundation, either version 3 of the License, or
//  (at your option) any later version.
//
//  This program is distributed in the hope that it will be useful,
//  but WITHOUT ANY WARRANTY; without even the implied warranty of
//  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
//  GNU Lesser General Public License for more details.
// 
//  You should have received a copy of the GNU Lesser General Public License
//  along with this program.  If not, see <http://www.gnu.org/licenses/>.

package hidra.many.metaheuristics.approaches.MOPSOGD;


import hidra.core.population.HIDRAPopulationAlgorithm;
import hidra.jmetal.core.Operator;
import hidra.jmetal.core.Problem;
import hidra.jmetal.core.Solution;
import hidra.jmetal.core.SolutionSet;
import hidra.qualityIndicator.QualityIndicator;

import java.util.Comparator;

import jmetal.util.JMException;
import jmetal.util.PseudoRandom;
import jmetal.util.comparators.DominanceComparator;
import jmetal.util.wrapper.XReal;

/**
 * This class implements the SMPSO algorithm described in:
 * A.J. Nebro, J.J. Durillo, J. Garcia-Nieto, C.A. Coello Coello, F. Luna and E. Alba
 * "SMPSO: A New PSO-based Metaheuristic for Multi-objective Optimization". 
 * IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making 
 * (MCDM 2009), pp: 66-73. March 2009
 */
public class MOPSOGD extends HIDRAPopulationAlgorithm {

  /**
   * Stores the number of particles_ used
   */
  private int swarmSize_;
  /**
   * Stores the maximum size for the archive
   */
  private int archiveSize_;
  /**
   * Stores the maximum number of iteration_
   */
 // private int maxIterations_;
  /**
   * Stores the current number of iteration_
   */
  //private int iteration_;
  /**
   * Stores the particles
   */
  private SolutionSet particles_;
  /**
   * Stores the best_ solutions founds so far for each particles
   */
  private Solution[] best_;
  /**
   * Stores the leaders_
   */
  private DominanceBoundArchive leaders_;
  /**
   * Stores the speed_ of each particle
   */
  private double[][] speed_;
  /**
   * Stores a comparator for checking dominance
   */
  private Comparator dominance_;
  /**
   * Stores a comparator for crowding checking
   */
  private Comparator comparatorGlobalDetriment_;
  
 
  double[] gmax; 
  
  /**
   * Stores a operator for non uniform mutations
   */
  private Operator polynomialMutation_;

  QualityIndicator indicators_; // QualityIndicator object

  double r1Max_;
  double r1Min_;
  double r2Max_;
  double r2Min_;
  double C1Max_;
  double C1Min_;
  double C2Max_;
  double C2Min_;
  double WMax_;
  double WMin_;
  double ChVel1_;
  double ChVel2_;

  /** 
   * Constructor
   * @param problem Problem to solve
   */
  public MOPSOGD(Problem problem) {
    super(problem) ;

    r1Max_ = 1.0;
    r1Min_ = 0.0;
    r2Max_ = 1.0;
    r2Min_ = 0.0;
    C1Max_ = 2.5;
    C1Min_ = 1.5;
    C2Max_ = 2.5;
    C2Min_ = 1.5;
    WMax_ = 0.1;
    WMin_ = 0.1;
    ChVel1_ = -1;
    ChVel2_ = -1;
  } // Constructor



  private double deltaMax_[];
  private double deltaMin_[];
  boolean success_;

 

  /**
   * Initialize all parameter of the algorithm
   */
  public void initParams() {
	  
	super.initParams();
	  
    swarmSize_ = ((Integer) getInputParameter("swarmSize")).intValue();
    archiveSize_ = ((Integer) getInputParameter("archiveSize")).intValue();
    maxIterations_ = ((Integer) getInputParameter("maxIterations")).intValue();

    indicators_ = (QualityIndicator) getInputParameter("indicators");

    polynomialMutation_ = operators_.get("mutation") ; 

    iteration_ = 0 ;

    success_ = false;

    particles_ = new SolutionSet(swarmSize_);
    best_ = new Solution[swarmSize_];
    
    
    gmax = new double[problem_.getNumberOfObjectives()];
    
    for(int k=0; k < problem_.getNumberOfObjectives() ; k++){
    	
    	gmax[k] = Double.NEGATIVE_INFINITY;
    	
    }
    
    leaders_ = new DominanceBoundArchive(archiveSize_, problem_.getNumberOfObjectives(), new GDComparator(), new GDFitness(gmax));

    // Create comparators for dominance and crowding distance
    dominance_ = new DominanceComparator();
    comparatorGlobalDetriment_ = new GDComparator();
    
    utils_ = new hidra.qualityIndicator.util.MetricsUtil();
    
    // Create the speed_ vector
    speed_ = new double[swarmSize_][problem_.getNumberOfVariables()];

    
    

    deltaMax_ = new double[problem_.getNumberOfVariables()];
    deltaMin_ = new double[problem_.getNumberOfVariables()];
    for (int i = 0; i < problem_.getNumberOfVariables(); i++) {
      deltaMax_[i] = (problem_.getUpperLimit(i) -
        problem_.getLowerLimit(i)) / 2.0;
      deltaMin_[i] = -deltaMax_[i];
    } // for
  } // initParams 

  // Adaptive inertia 
  private double inertiaWeight(int iter, int miter, double wma, double wmin) {
    return wma; // - (((wma-wmin)*(double)iter)/(double)miter);
  } // inertiaWeight

  // constriction coefficient (M. Clerc)
  private double constrictionCoefficient(double c1, double c2) {
    double rho = c1 + c2;
    //rho = 1.0 ;
    if (rho <= 4) {
      return 1.0;
    } else {
      return 2 / (2 - rho - Math.sqrt(Math.pow(rho, 2.0) - 4.0 * rho));
    }
  } // constrictionCoefficient


  // velocity bounds
  private double velocityConstriction(double v, double[] deltaMax,
                                      double[] deltaMin, int variableIndex,
                                      int particleIndex)  {


    double result;

    double dmax = deltaMax[variableIndex];
    double dmin = deltaMin[variableIndex];

    result = v;

    if (v > dmax) {
      result = dmax;
    }

    if (v < dmin) {
      result = dmin;
    }

    return result;
  } // velocityConstriction

  /**
   * Update the speed of each particle
   * @throws JMException 
   */
  private void computeSpeed(int iter, int miter) throws JMException {
    double r1, r2, W, C1, C2;
    double wmax, wmin, deltaMax, deltaMin;
    XReal bestGlobal;

    for (int i = 0; i < swarmSize_; i++) {
    	XReal particle = new XReal(particles_.get(i)) ;
    	XReal bestParticle = new XReal(best_[i]) ;

      //Select a global best_ for calculate the speed of particle i, bestGlobal
      Solution one, two;
      int pos1 = PseudoRandom.randInt(0, leaders_.size() - 1);
      int pos2 = PseudoRandom.randInt(0, leaders_.size() - 1);
      one = leaders_.get(pos1);
      two = leaders_.get(pos2);

      if (comparatorGlobalDetriment_.compare(one, two) > 0 ) {
        bestGlobal = new XReal(one);
      } else {
        bestGlobal = new XReal(two);
      //Params for velocity equation
      }
      r1 = PseudoRandom.randDouble(r1Min_, r1Max_);
      r2 = PseudoRandom.randDouble(r2Min_, r2Max_);
      C1 = PseudoRandom.randDouble(C1Min_, C1Max_);
      C2 = PseudoRandom.randDouble(C2Min_, C2Max_);
      W = PseudoRandom.randDouble(WMin_, WMax_);
      //
      wmax = WMax_;
      wmin = WMin_;

      for (int var = 0; var < particle.getNumberOfDecisionVariables(); var++) {
        //Computing the velocity of this particle 
        speed_[i][var] = velocityConstriction(constrictionCoefficient(C1, C2) *
          (/*inertiaWeight(iter, miter, wmax, wmin)  */ W *
          speed_[i][var] +
          C1 * r1 * (bestParticle.getValue(var) -
          particle.getValue(var)) +
          C2 * r2 * (bestGlobal.getValue(var) -
          particle.getValue(var))), deltaMax_, //[var],
          deltaMin_, //[var], 
          var,
          i);
      }
    }
  } // computeSpeed

  /**
   * Update the position of each particle
   * @throws JMException 
   */
  private void computeNewPositions() throws JMException {
    for (int i = 0; i < swarmSize_; i++) {
    	XReal particle = new XReal(particles_.get(i)) ;
      for (int var = 0; var < particle.getNumberOfDecisionVariables(); var++) {
      	particle.setValue(var, particle.getValue(var) +  speed_[i][var]) ;
      	
        if (particle.getValue(var) < problem_.getLowerLimit(var)) {
          particle.setValue(var, problem_.getLowerLimit(var));
          speed_[i][var] = speed_[i][var] * ChVel1_; //    
        }
        if (particle.getValue(var) > problem_.getUpperLimit(var)) {
          particle.setValue(var, problem_.getUpperLimit(var));
          speed_[i][var] = speed_[i][var] * ChVel2_; //   
        }
      }
    }
  } // computeNewPositions

  /**
   * Apply a mutation operator to some particles in the swarm
   * @throws JMException 
   */
  private void mopsoMutation(int actualIteration, int totalIterations) throws JMException {
    for (int i = 0; i < particles_.size(); i++) {
      if ( (i % 6) == 0)
        polynomialMutation_.execute(particles_.get(i)) ;
      //if (i % 3 == 0) { //particles_ mutated with a non-uniform mutation %3
      //  nonUniformMutation_.execute(particles_.get(i));
      //} else if (i % 3 == 1) { //particles_ mutated with a uniform mutation operator
      //  uniformMutation_.execute(particles_.get(i));
      //} else //particles_ without mutation
      //;
    }
  } // mopsoMutation

  
  
  @Override
  protected SolutionSet getParetoFront() {
  	// TODO Auto-generated method stub
  	return this.leaders_;
  }

  @Override
  protected SolutionSet initializationAlgorithm() throws ClassNotFoundException,
  		JMException {
  	
	   initParams();

	    success_ = false;
	    //->Step 1 (and 3) Create the initial population and evaluate
	    for (int i = 0; i < swarmSize_; i++) {
	      Solution particle = new Solution(problem_);
	      problem_.evaluate(particle);
	      problem_.evaluateConstraints(particle);
	      particles_.add(particle);
	    }

	    
	    double[] objectives = utils_.getMaximumValues(particles_.writeObjectivesToMatrix(), problem_.getNumberOfObjectives());
		
	    for(int i=0; i < problem_.getNumberOfObjectives() ; i++){			
			gmax[i] = objectives[i];
	    }
	    
	  
	    
	    //-> Step2. Initialize the speed_ of each particle to 0
	    for (int i = 0; i < swarmSize_; i++) {
	      for (int j = 0; j < problem_.getNumberOfVariables(); j++) {
	        speed_[i][j] = 0.0;
	      }
	    }


	    // Step4 and 5   
	    for (int i = 0; i < particles_.size(); i++) {
	      Solution particle = new Solution(particles_.get(i));
	      leaders_.add(particle);
	    }

	    //-> Step 6. Initialize the memory of each particle
	    for (int i = 0; i < particles_.size(); i++) {
	      Solution particle = new Solution(particles_.get(i));
	      best_[i] = particle;
	    }

	    //Crowding the leaders_
	    GDFitness fitness = new GDFitness(gmax);
	    fitness.fitnessAssign(this.leaders_, problem_.getNumberOfObjectives());

	    return this.leaders_;	  	  
  }

  
  hidra.qualityIndicator.util.MetricsUtil utils_; 
  
  @Override
  protected SolutionSet runIteration() throws JMException {
  	        //Compute the speed_
	      computeSpeed(iteration_, maxIterations_);
	     
	      //Compute the new positions for the particles_            
	      computeNewPositions();

	      //Mutate the particles_          
	      mopsoMutation(iteration_, maxIterations_);

	      //Evaluate the new particles_ in new positions
	      for (int i = 0; i < particles_.size(); i++) {
	        Solution particle = particles_.get(i);
	        problem_.evaluate(particle);
	      }

	      double[] objectives = utils_.getMaximumValues(this.particles_.writeObjectivesToMatrix(), problem_.getNumberOfObjectives());
			
		  for(int i=0; i < problem_.getNumberOfObjectives() ; i++){
				gmax[i] = Math.max(objectives[i], gmax[i]);				
		  }
	      
	      
	      //Actualize the archive          
	      for (int i = 0; i < particles_.size(); i++) {
	        Solution particle = new Solution(particles_.get(i));
	        leaders_.add(particle);
	      }
	      
	      //Assign crowding distance to the leaders_
		  //Crowding the leaders_
		  GDFitness fitness = new GDFitness(gmax);
		  fitness.fitnessAssign(this.leaders_, problem_.getNumberOfObjectives());
		     

	      //Actualize the memory of this particle
	      for (int i = 0; i < particles_.size(); i++) {
	        int flag = dominance_.compare(particles_.get(i), best_[i]);
	        if (flag == -1) { // the new particle is best_ than the older remeber        
	          Solution particle = new Solution(particles_.get(i));
	          best_[i] = particle;
	        }if (flag == 0){
	        	Solution particle = new Solution(particles_.get(i));
	        	fitness = new GDFitness(gmax);
	        	double fitness1 = fitness.globalDetritment(particle,  best_[i], problem_.getNumberOfObjectives());
	        	//particle.setFitness(fitness1);
	        	double fitness2 = fitness.globalDetritment(best_[i],particle,problem_.getNumberOfObjectives());
	        	//best_[i].setFitness(fitness2);	        		        	
	        	if(fitness1 < fitness2){// the new particle is best_ than the older remeber        	  	         
		          best_[i] = particle;	        		
	        	}
	        	
	        }
	      
	      }

	      
	      
	      iteration_++;

	      return this.leaders_;
	  
  }

  
  
  
  
  /** 
   * Gets the leaders of the SMPSO algorithm
   */
  public SolutionSet getLeader() {
    return leaders_;
  }  // getLeader   




} // SMPSO
